Stock Picking Basics





Kerry Back

Simple Valuation Model

Growing perpetuity

  • Cash flows \(C_1 = c\), \(C_2 = (1+g)c\), \(C_3 = (1+g)^2c\) and so on forever.
  • Discount rate \(r>g\)
  • PV is

\[ c\left[\frac{1}{1+r} + \frac{1+g}{(1+r)^2} + \frac{(1+g)^2}{(1+r)^3} + \cdots\right] = \frac{c}{r-g}\]

Gordon growth model

  • We want to value cash flows to shareholders
  • \(r=\) required return on equity
  • Payouts to shareholders = dividends + repurchases - net issues
  • Assume earnings, payouts, and book equity all grow at rate \(g<r\).
  • Define ROE to be earnings divided by lagged (beginning of year) equity.
  • Set \(k =\) payout ratio \(=\) payouts / earnings.

  • Equity grows by earnings minus payouts = \((1-k) \times\) earnings.
  • Earnings \(=\) ROE \(\times\) lagged equity.
  • \(g=\) % change in equity \(=\) growth in equity / lagged equity

\[=\frac{(1-k) \times \text{ROE} \times \text{lagged equity}}{\text{lagged equity}}\]

\[= (1-k) \times \text{ROE}\]

  • Value of stock is next year’s payout / \((r-g)\).
  • Next year’s payout is \(k\) \(\times\) next year’s earnings.
  • Next year’s earnings \(=\) ROE \(\times\) current book equity.
  • Theoretical price-to-book \(=\) market-to-book

\[=\left.\frac{k \times \text{ROE} \times \text{book equity}}{r-(1-k)\times \text{ROE}}\right/ \text{book equity}\]

\[=\frac{k \times \text{ROE}}{r-(1-k)\times \text{ROE}}\]

Dupont Analysis

\[\text{ROE} = \frac{\text{Net Income}}{\text{Sales}} \times \frac{\text{Sales}}{\text{Lagged Assets}} \] \[\times \frac{\text{Lagged Assets}}{\text{Lagged Equity}}\]


\[= \text{Profit Margin} \times \text{Asset Turnover}\] \[ \times \text{Leverage}\]

Security Analysis

Sell side and buy side

  • Sell-side analysts work for brokerage firms and provide research to brokerage clients.
    • They are a cost center and research is provided free to generate business (= commissions or advising fees).
  • Buy-side analysts work for investment funds who use the research to pick stocks.

Technical versus fundamental

  • Fundamental analysts forecast important ratios and growth rates to produce earnings forecasts and price targets as in Gordon/Dupont.
  • Technical analysts use past prices to generate recommendations.

Technical analysis examples

  • Support and resistance levels.
    • Previous minimum (support) and maximum (resistance) stock prices are regarded as difficult to breach.
    • But if breached, the trend is expected to continue.
  • Moving averages: buy when price rises above moving average and sell when it falls below.
  • Chart patterns (head and shoulders, …)

Quantitative Investing

  • Quantitative investing means using quantifiable signals to pick stocks and/or to time the market.
  • Signals can include ratios and growth rates used by fundamental analysts and price signals used by technical analysts.
  • Signals can also include
    • insider trades, short interest, …
    • sentiment analysis of social media, traditional media, and company announcements
    • satellite and drone image data, and …

Efficient Markets Hypothesis

  • All relevant information is already impounded into prices.
    • Fundamental analysis is futile.
    • Technical analysis is futile.
  • Higher expected returns come only with higher risks: arket risk (beta) and/or other types of risks (oil price, …)

Counter-argument

  • Not all investors are smart
  • Smart investors may not scoop up all opportunities
    • Limited capital
    • Costs of trading
    • For example, an investor who shorts risks running out of capital from margin calls before being eventually right.
  • More likely to be opportunities among smaller stocks, which are difficult for large investors to trade.

Smart beta (factor) investing

  • Groups of stocks with certain characteristics seem to have higher expected returns.
  • These stocks also usually tend to move together.
  • Maybe they are exposed to some risk that some investors regard as undesirable.
  • Maybe you want to take on that risk to get the return.

  • The return of the group of stocks is called a factor.
  • Investing in the factor means you will be correlated with the factor.
    • So, if we regress your return on the factor, you will have a positive slope coefficient (beta).
    • Hence the name “smart beta.”
  • Example: Fama-French factors: Small Minus Big, High book-to-market Minus Low book-to-market, Conservative Minus Agressive, Robust Minus Weak.

  • The “exposed to some risk” story is a way to reconcile factor investing with the efficient markets hypothesis.
  • It is also possible that stocks are just mispriced in systematic ways.
  • For example, there is evidence that analysts recognize that “quality” stocks are worth more than “junk” stocks, but they underestimate how much more.

Industry examples



Factor investing at BlackRock


Factor investing at AQR

Some data

  • Sort into quintiles each month.
  • Value weighted return of each group
  • Re-sort at the beginning of the next period and continue.

Factor investing with machine learning

  • Find factors worth investing in.
  • Decide how to optimally combine them.
  • Using ML, we can in principle throw in lots of characteristics and let the machine decide which are useful, but preprocessing is usually useful.
  • Need to backtest, which is a variation of the usual ML train-and-test.
  • Gu, Kelly, and Xiu, 2020